Open Access
ITM Web Conf.
Volume 21, 2018
Computing in Science and Technology (CST 2018)
Article Number 00002
Number of page(s) 9
Published online 12 October 2018
  1. E. Dudek-Dyduch, Intelligent ALMM System for Discrete Optimization Problems – the Idea of Knowledge Base Application, ISAT, Vol. 657, (2017) [Google Scholar]
  2. E. Dudek-Dyduch, S. Korzonek: ALMM Solver for combinatorial and discrete optimization problems Idea of Problem Model Library, ACIIDS Part I. LNAI, vol. 9621, (2016) [Google Scholar]
  3. S. Korzonek, E. Dudek-Dyduch, Component Library of Problem Models for ALMM Solver, Journal of Information and Telecommunication, 1:3, pp. 224–240, (2017) [CrossRef] [Google Scholar]
  4. E. Dudek-Dyduch,: Modeling Manufacturing Processes with Disturbances a New Method Based on Algebraic-Logical Meta-Model. ICAISC, Part II. LNCS, vol. 9120, (2015) [Google Scholar]
  5. E. Dudek-Dyduch, E. Kucharska, L. Dutkiewicz, K. Rączka: ALMM Solver-A Tool for Optimization Problems. ICAISC 2014, pp. 328–338 (2014) [Google Scholar]
  6. E. Dudek-Dyduch, E. Kucharska: Learning method for co-operation. ICCCI 2011 Part II. LNCS, vol. 6923, pp. 290–300. Springer, Heidelberg, (2011) [Google Scholar]
  7. E. Dudek-Dyduch, L. Dutkiewicz: Substitution tasks method for discrete optimization. ICAISC 2013, Part II. LNCS, vol. 7895, pp. 419–430, (2013) [Google Scholar]
  8. E. Dudek-Dyduch, E. Kucharska: Optimization Learning Method for Discrete Process Control. In: ICINCO 2011, Vol. 1, pp. 24–33, (2011) [Google Scholar]
  9. E. Dudek-Dyduch, E. Kucharska, L. Dutkiewicz, K. Rączka: ALMM Solver-A Tool for Optimization Problems. ICAISC 2014, pp. 328–338, (2014) [Google Scholar]
  10. L. Dutkiewicz, E. Dudek-Dyduch: Substitution Tasks Method for Co-operation. In: Recent Developments in Computational Collective Intelligence, pp. 103–113, (2014) [CrossRef] [Google Scholar]
  11. E. Dudek-Dyduch: Learning based algorithm in scheduling. Journal of Intelligent Manufacturing (JIM), Vol. 11, no 2, pp. 135–143., (2000) [Google Scholar]
  12. E. Dudek-Dyduch,: Problems of knowledge representation in expert system aided control of DPP (in Polish), part I, pp. 147–154, Wrocław, (1993) [Google Scholar]
  13. E. Dudek-Dyduch,: Algebraic Logical Meta-Model of Decision Processes New Metaheuristics. ICAISC, Part 1. LNCS, vol. 9119, pp. 541–554, (2015) [Google Scholar]
  14. E. Dudek-Dyduch,: Modeling Manufacturing Processes with Disturbances – Two-Stage AL Model Transformation Method, MMAR, pp. 782–787 (2015) [Google Scholar]
  15. E. Dudek-Dyduch: Discrete determinable processes compact knowledge-based model, Notas de Matematica No 137, Universidad de Los Andes Venezuela, (1993) [Google Scholar]
  16. E. Dudek-Dyduch: Formalization and analysis of problems of discrete manufacturing processes. Scientific bulletin of AGH University, Automatics Vol. 54, (in Polish), (1990) [Google Scholar]
  17. K. Rączka, E. Dudek-Dyduch, E. Kucharska, L. Dutkiewicz: ALMM Solver: the Idea and the Architecture. In: Rutkowski at al. (Eds.) ICAISC 2015, Part II. LNCS, vol. 9120, pp. 504–514, Springer International Publishing, (2015) [Google Scholar]
  18. P. Jędrzejowicz, E. Ratajczak-Ropel: Reinforcement Learning Strategy for Solving the MRCPSP by a Team of Agents, Intelligent Decision Technologies, Smart Innovation, Systems and Technologies, Vol. 39, Springer, pp. 537–548, (2015) [CrossRef] [Google Scholar]
  19. E. Dudek-Dyduch: Information systems for production management (in Polish) Wyd. Poldex, Kraków ISBN 83-88979-12-4,(2002) [Google Scholar]
  20. L Anselma,. L. Piovesan, A. Sattar, B. Stantic, A. Paolo Terenzian, Comprehensive Approach to ‘Now’ in Temporal Relational Databases: Semantics and Representation, IEEE Transactions On Knowledge And Data Engineering, Vol.: 28, Issue: 10, pp: 2538–2551 [Google Scholar]
  21. E. Dudek-Dyduch, T. Dyduch: Formal approach to optimization of discrete manufacturing processes. in: Hamza, M.H. Proc. of the Twelfth IASTED, Acta Press, Zurich, (1993) [Google Scholar]
  22. F. Rossi, P. Van Beek, T. Walsh,: Handbook of Constraint Programming, Elsevier, (2006) [Google Scholar]
  23. P. Terenziani, Nearly Periodic Facts in Temporal Relational Databases, IEEE Transactions on Knowledge and Data Engineering, Volume: 28, Issue: 10, Pages: 2822–2826, (2016) [CrossRef] [Google Scholar]
  24. E. Kucharska, E. Dudek-Dyduch: Extended Learning Method for Designation of Cooperation. In: Transactions on Computational Collective Intelligence XIV, pp. 136–157, (2014) [Google Scholar]
  25. J. M. Medina, C. D. Barranco, O. Pons, Evaluation of Indexing Strategies for Possibilistic Queries Based on Indexing Techniques Available in Traditional RDBMS, (2016) [Google Scholar]
  26. F. Abdelhedi, A.A. Brahim, F. Atigui, G. Zurfluh, Big Data and Knowledge Management: How to Implement Conceptual Models in NoSQL Systems?, Knowledge Engineering And Knowledge Management, vol. 3 (KMIS), pp: 235–240, (2016) [Google Scholar]
  27. J Błażewicz., K. Ecker, E. Pesch, G. Schmidt, J. Węglarz: Handbook on Scheduling. Springer Berlin Heidelberg New York, ISBN 978-3-540-28046-0, (2007) [Google Scholar]
  28. R.E. Smith, N. Taylor: A Framework for Evolutionary Computation in Agent-Based Systems, Proc. of Int. Conf. on Intelligent Systems, pp. 221–224. 1SCA Press, (1998) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.